Portable energy storage power supply evaluation method and system

Abstract

The invention relates to the technical field of data analysis, in particular to a portable energy storage power supply evaluation method and system, and the method comprises the following steps: collecting the voltage, current, temperature and charge and discharge frequency data of a power supply through a sensor, recording a data flow in a local database in real time, analyzing the current working state of the power supply, and generating real-time performance monitoring data. According to the invention, the fine change of the battery capacity can be more accurately captured by monitoring the power supply performance and environmental factors in real time, so that the future capacity change of the energy storage power supply can be more accurately predicted, and meanwhile, the environmental temperature and humidity data are taken into consideration, so that the power supply is allowed to be subjected to more accurate performance evaluation and optimization; key external variables influencing the battery performance can be recognized, so that the key external variables are considered when a power supply use strategy is designed, optimal operation of a power supply system is ensured, dynamic adjustment of a charging and discharging strategy reflects quick response to real-time data feedback, the overall performance of the power supply is optimized, and the service life of the power supply is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a portable energy storage power supply evaluation method and system. Background Art

[0002] The field of data analysis technology involves methods and techniques for extracting useful information from raw data, inferring patterns and associations, and performing statistical analysis on data. It uses algorithms and software tools to process, interpret, and visualize data, as well as perform predictive modeling. Especially in the field of energy management, data analysis helps optimize resource allocation, improve energy efficiency, and support sustainable development strategies. Key tools include machine learning, statistical modeling, data mining, and big data technology, all of which are designed to better understand data sets and make more effective decisions.

[0003] Among them, the portable energy storage power supply evaluation method is mainly used to evaluate the performance and efficiency of portable energy storage devices (such as battery packs), and the main focus is on how to accurately measure the output capacity, durability, charging and discharging efficiency and overall reliability of the power supply. The purpose of the evaluation is to ensure that the portable energy storage power supply can meet specific performance standards and safety requirements in actual use. By using data analysis technology, insights can be extracted from equipment operation data to predict the performance of the equipment, thereby helping manufacturers and users make more informed decisions. It is also an indispensable part of the product development and quality assurance process, especially in the growing market for electronic and mobile devices.

[0004] Existing technologies usually rely on static data analysis and periodic testing in the evaluation process of portable energy storage power supplies, lacking rapid response to real-time data and environmental changes, resulting in the inability to adjust power management strategies in a timely manner under rapidly changing environmental conditions, thereby affecting the efficiency and life of the power supply. For example, failure to monitor and adjust charging and discharging strategies in real time may cause the battery to overcharge or discharge, thereby accelerating the battery aging process and reducing the overall reliability and safety of the power supply. In addition, traditional methods often fail to make full use of environmental data, resulting in inaccurate evaluation of power supply performance under extreme climate conditions, which is particularly critical in power-intensive mobile devices and electric vehicle industries, increasing maintenance costs and usage risks. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a portable energy storage power supply evaluation method.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a portable energy storage power supply evaluation method, comprising the following steps:

[0007] S1: Collect power supply voltage, current, temperature and charge and discharge frequency data through sensors, record data streams in real time in the local database, analyze the current working status of the power supply, and generate real-time performance monitoring data;

[0008] S2: Calculate the changes in voltage and current of the battery during each charge and discharge through the real-time performance monitoring data, draw a power capacity attenuation curve, separate key attenuation trends according to the power capacity attenuation curve, predict future capacity changes of the power source, and generate a power capacity attenuation trend prediction result;

[0009] S3: Using the power capacity attenuation trend prediction result, synchronously collect environmental temperature and humidity data, analyze the correlation between environmental factors and battery performance attenuation, test the difference of energy storage power supply performance under different environmental conditions according to the analysis results, confirm the actual impact of the difference factors on power supply performance, and form an environmental impact analysis record;

[0010] S4: Through the environmental impact analysis records, the charging and discharging strategies of the energy storage power supply are dynamically adjusted, and improvement measures are designed for key problem points to form an optimization plan for the portable energy storage power supply.

[0011] As a further solution of the present invention, the step of acquiring the real-time performance monitoring data is:

[0012] S111: deploy sensors to measure the voltage, current, temperature and charge and discharge frequency of the power supply in real time to obtain a real-time power supply data set;

[0013] S112: uploading the real-time power data set to a local database, marking a timestamp for each piece of data, and obtaining a power data record with a timestamp;

[0014] S113: Clean and analyze the data through the power supply data record with time stamp, using the formula:

[0015]

[0016] Calculate the fluctuation rate P of power supply performance and obtain real-time performance monitoring data, where a i represents a single data point, μ represents the data mean, and N represents the total number of data points.

[0017] As a further solution of the present invention, the step of drawing the power capacity attenuation curve is:

[0018] S211: extracting the real-time voltage and current data of the battery under the charging and discharging state based on the real-time performance monitoring data to obtain a real-time voltage and current data set;

[0019] S212: Using the voltage and current real-time data set, using the formula,

[0020]

[0021] and

[0022]

[0023] Calculate the rate of change of voltage and current in each charge and discharge cycle to obtain the change sequence data of voltage and current, where V i and I i Represent the voltage and current values ​​measured for the ith time, V i+1 is the voltage measurement at time i+1, I i+1 is the current measurement at time i+1, ΔV i Represents the voltage change rate from time i to time i+1, ΔI i Represents the square root ratio of the current change from time i to time i+1;

[0024] S213: Perform a multivariate linear regression analysis using the voltage and current variation sequence data, and draw and output a power capacity attenuation curve in combination with the historical capacity data of the battery and environmental factors.

[0025] As a further solution of the present invention, the step of obtaining the power capacity attenuation trend prediction result is:

[0026] S221: Performing data analysis on the power capacity attenuation curve, identifying mutation points and trend turning points on the curve, revealing the moment of battery performance mutation, and generating key change point data;

[0027] S222: Analyze the data trends of the different time periods based on the key change point data, separate the long-term and short-term decay trends, and generate separated key decay trend data;

[0028] S223: Based on the separated key attenuation trend data, predict future capacity changes, analyze and determine future attenuation paths, and obtain power capacity attenuation trend prediction results.

[0029] As a further solution of the present invention, the analysis step of the correlation between the environmental factors and the battery performance degradation is:

[0030] S311: synchronously collecting the current ambient temperature and humidity based on the power capacity attenuation trend prediction result to obtain synchronous environmental data;

[0031] S312: Using the synchronization environment data, a correlation analysis is performed using the formula:

[0032]

[0033] Calculate the correlation coefficient r between the ambient temperature and humidity and the battery capacity decay rate to obtain a correlation coefficient matrix, where x and y represent the data values ​​of the collected environmental data and the battery performance decay rate, and n is the number of data points;

[0034] S313: Using the correlation coefficient matrix, identify key environmental factors that affect battery performance degradation, and form a correlation analysis record between environmental factors and battery performance degradation.

[0035] As a further solution of the present invention, the step of obtaining the environmental impact analysis record is:

[0036] S321: Organizing a differentiated environmental experiment based on the correlation analysis record between the environmental factors and battery performance degradation, recording the experimental data in real time and automatically storing them, and obtaining a differentiated experimental record;

[0037] S322: performing statistical analysis on the collected power supply performance data according to the difference experiment record, evaluating the impact of the change of the difference environmental conditions on the power supply performance, and obtaining the environmental condition statistical analysis result;

[0038] S323: Based on the statistical analysis results of the environmental conditions, the changes in power supply performance under each environmental condition and the actual impact of environmental factors are sorted out, and an environmental impact analysis record is output.

[0039] As a further solution of the present invention, the steps for obtaining the portable energy storage power supply optimization solution are:

[0040] S411: Using the environmental impact analysis record, evaluate the impact of different environmental factors on the charging and discharging efficiency of the energy storage power supply, extract the charging and discharging data under each environment, and use the formula:

[0041]

[0042] Calculate the average efficiency η under each condition and obtain the efficiency analysis results, where c i is the charging efficiency under the i-th environment, d i is the discharge efficiency under the ith environment, v i is the voltage under the target environmental condition, M is the number of differential environmental conditions;

[0043] S412: according to the efficiency analysis result, a comparison threshold is set to determine the key environmental conditions with low efficiency, and the charging and discharging parameters of the power supply are adjusted according to the corresponding environmental conditions to match the difference environmental conditions and obtain an adjustment strategy;

[0044] S413: According to the adjustment strategy, the structure and control strategy of the portable energy storage power supply are redesigned, and the actual impact on the performance is evaluated to form an optimization plan for the portable energy storage power supply.

[0045] A portable energy storage power supply evaluation system, comprising:

[0046] The real-time monitoring module deploys sensors to measure the voltage, current, temperature and charge and discharge frequency of the power supply in real time, mark each data with a timestamp, calculate the fluctuation rate of the power supply performance, and obtain real-time performance monitoring data;

[0047] The state analysis module extracts the real-time voltage and current data of the battery under the charge and discharge state based on the real-time performance monitoring data, calculates the rate of change of the voltage and current in each charge and discharge cycle, and draws and outputs the power capacity attenuation curve in combination with the historical capacity data of the battery and environmental factors;

[0048] The trend prediction module performs data analysis on the power capacity attenuation curve, identifies mutation points and trend turning points on the curve, analyzes data trends in different time periods, analyzes and determines future attenuation paths, and obtains power capacity attenuation trend prediction results;

[0049] The environment correlation module collects the current environment temperature and humidity synchronously through the power capacity attenuation trend prediction result, performs correlation analysis, identifies key environment factors that affect battery performance attenuation, and outputs the correlation analysis record between environment factors and battery performance attenuation;

[0050] The experimental evaluation module organizes differentiated environmental experiments based on the correlation analysis records between the environmental factors and battery performance degradation, performs statistical analysis, evaluates the impact of changes in differential environmental conditions on power performance, and organizes and outputs environmental impact analysis records;

[0051] The optimization scheme module uses the environmental impact analysis records to evaluate the impact of different environmental factors on the charging and discharging efficiency of the energy storage power supply, calculates the average efficiency under each condition, adjusts the charging and discharging parameters of the power supply, matches the different environmental conditions, redesigns the portable energy storage power supply structure and control strategy, and forms a portable energy storage power supply optimization scheme.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are:

[0053] In the present invention, by real-time monitoring of power supply performance and environmental factors, it is possible to more accurately capture subtle changes in battery capacity, thereby making more accurate predictions of future capacity changes of the energy storage power supply. At the same time, environmental temperature and humidity data are taken into consideration to refine the understanding of the impact on power supply performance, allowing for more accurate performance evaluation and optimization of the power supply, and being able to identify key external variables that affect battery performance, thereby taking them into consideration when designing power supply usage strategies to ensure optimal operation of the power supply system. The dynamic adjustment of the charge and discharge strategy reflects a rapid response to real-time data feedback, thereby optimizing the overall performance of the power supply and extending the service life of the power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a main step flow chart of the present invention;

[0055] Figure 2 This is a flowchart for obtaining real-time performance monitoring data of the present invention;

[0056] Figure 3 A flow chart for drawing a power capacity attenuation curve of the present invention;

[0057] Figure 4 A flowchart for obtaining the power capacity attenuation trend prediction result of the present invention;

[0058] Figure 5 The flowchart of the analysis of the correlation between environmental factors and battery performance degradation of the present invention;

[0059] Figure 6 A flow chart for obtaining the environmental impact analysis record of the present invention;

[0060] Figure 7 This is a flow chart for obtaining the portable energy storage power supply optimization solution of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0063] See also Figure 1 , a portable energy storage power supply evaluation method, comprising the following steps:

[0064] S1: Collect power supply voltage, current, temperature and charge and discharge frequency data through sensors, record data streams in real time in the local database, analyze the current working status of the power supply, and generate real-time performance monitoring data;

[0065] S2: Calculate the changes in voltage and current of the battery during each charge and discharge through real-time performance monitoring data, draw a power capacity attenuation curve, separate key attenuation trends based on the power capacity attenuation curve, and predict future capacity changes of the power supply to generate a power capacity attenuation trend prediction result;

[0066] S3: Using the power capacity attenuation trend prediction results, collect environmental temperature and humidity data simultaneously, analyze the correlation between environmental factors and battery performance attenuation, test the differences in energy storage power supply performance under different environmental conditions based on the analysis results, confirm the actual impact of the difference factors on power supply performance, and form an environmental impact analysis record;

[0067] S4: Through environmental impact analysis records, dynamically adjust the charging and discharging strategies of the energy storage power supply, design improvement measures for key problem points, and form an optimization plan for portable energy storage power supply.

[0068] Real-time performance monitoring data includes energy storage power supply voltage index, current index, temperature index and frequency index; power supply capacity attenuation trend prediction results include attenuation curve graph, key trend points and future capacity prediction results; environmental impact analysis records include ambient temperature change impact analysis results, ambient humidity change impact analysis results and performance difference verification results; portable energy storage power supply optimization plan includes charging strategy adjustment records, discharge strategy adjustment records and improvement measures design results.

[0069] See also Figure 2 ,The steps to obtain real-time performance monitoring data are,

[0070] S111: deploy sensors to measure the voltage, current, temperature and charge and discharge frequency of the power supply in real time to obtain a real-time power supply data set;

[0071] Specific sensors are deployed in the power supply system to monitor the key parameters of the power supply in real time through high-precision sensors, including voltage, current, temperature and charge and discharge frequency. In order to ensure the accuracy and reliability of the data, the sensors used have the characteristics of low noise and high sensitivity. The interface between the sensor and the power supply system is specially designed to resist high temperature and electromagnetic interference, ensuring stable operation in various working environments. Real-time data is transmitted to the data receiving system through an encrypted wireless network. Each data point is time-synchronized during the transmission process to ensure the synchronization and timing accuracy of data acquisition. For the collected raw data, preliminary signal condition processing is first performed, such as filtering and denoising, to eliminate potential interference from the environment and equipment operation. The optimized data provides an accurate basis for subsequent analysis. Each operation step is strictly performed in accordance with the power supply monitoring standard to ensure that the obtained real-time power supply data set can accurately reflect the actual operating status of the power supply.

[0072] S112: uploading the real-time power data set to the local database, marking each data with a timestamp, and obtaining a power data record with a timestamp;

[0073] The obtained real-time power data set is uploaded to the local database, and the data received from the sensor is formatted and batch processed to adapt to the storage format of the database. The data is compressed and encrypted before uploading to ensure the security and integrity of the data during transmission. Before uploading to the database, each piece of data will be attached with a timestamp accurate to milliseconds. This time mark is provided by the server's time synchronization module to ensure the uniformity and accuracy of the data timestamp. The database system uses efficient data indexing and query optimization technology to ensure that data query and analysis requests can be responded to quickly even when the data volume is extremely large. In addition, the database also sets up multi-level access control to ensure that only authorized users can access sensitive data, which jointly ensures the efficiency and data security of the data processing process, and provides strong data support for subsequent data analysis and power status monitoring.

[0074] S113: Through the power data recording with time stamp, the data is cleaned and analyzed, using the formula,

[0075]

[0076] Calculate the fluctuation rate P of power supply performance and obtain real-time performance monitoring data, where a i represents a single data point, μ represents the data mean, and N represents the total number of data points;

[0077] There are data points 100, 102, 98, 101, 103.

[0078] First calculate the mean:

[0079]

[0080] Calculate the square of the difference between each data point and the mean, that is:

[0081] (100-100.8) 2 =0.64

[0082] (102-100.8) 2 =1.44

[0083] (98-100.8) 2 =7.84

[0084] (101-100.8) 2 =0.04

[0085] (103-100.8) 2 =4.84

[0086] Then the standard deviation is:

[0087]

[0088] The results show the degree of fluctuation of the power supply data around the average value (the quantitative value is 1.72). In power supply performance monitoring, if the standard deviation value is less than the maximum fluctuation range allowed by the power supply design, then the degree of fluctuation can be considered to be within the controllable range, that is, the fluctuation is small; if it exceeds the design standard or safety standard, then the degree of fluctuation may be considered large and may require further investigation or improvement measures, which is crucial for analyzing the stability of the power supply and predicting potential power supply problems.

[0089] See also Figure 3 , the steps for drawing the power capacity attenuation curve are:

[0090] S211: extracting real-time voltage and current data of the battery under charging and discharging conditions based on the real-time performance monitoring data to obtain a real-time data set of voltage and current;

[0091] Through real-time performance monitoring data, the voltage and current fluctuations of the battery during the charging and discharging process are monitored in real time. The sensor collects multiple data points per second to ensure the continuity and accuracy of the data. The collected raw data is pre-processed by the embedded system, including filtering and outlier removal. The pre-processed data is sent to the data acquisition server through a secure network protocol. The server timestamps and stores the data in batches and stores it in the cloud database for further analysis. At the same time, the monitoring system is also equipped with a data quality detection mechanism. For example, if the data fluctuations exceed the preset threshold for three consecutive times, it will be automatically marked as abnormal and the technician will be notified to check the sensor status or battery health to ensure the accuracy and reliability of the data. The voltage and current real-time data set finally generated is the basic data source for battery performance analysis and capacity decay prediction.

[0092] S212: Using the voltage and current real-time data set, the formula

[0093]

[0094] and

[0095]

[0096] Calculate the rate of change of voltage and current in each charge and discharge cycle to obtain the change sequence data of voltage and current, where V i and I i Represent the voltage and current values ​​measured for the ith time, V i+1 is the voltage measurement at time i+1, I i+1 is the current measurement at time i+1, ΔVi Represents the voltage change rate from time i to time i+1, ΔI i Represents the square root ratio of the current change from time i to time i+1;

[0097] The voltage V measured at a certain point in time i is 3.7V, and the voltage V i+1 is 3.9V, current I i is 1.2A, and the current at the next time point I i+1 is 1.3A. Then, the voltage change rate ΔV i Calculated as:

[0098]

[0099] Calculation of current change rate:

[0100]

[0101] Furthermore, the specific values ​​of voltage and current changes in each charge and discharge cycle can be obtained, which is critical for analyzing battery performance degradation and indicates the stability of battery voltage and current during continuous use.

[0102] S213: Perform multiple linear regression analysis using the voltage and current change sequence data, and draw and output a power capacity attenuation curve by combining the battery's historical capacity data and environmental factors;

[0103] Using the collected voltage and current change sequence, combined with the battery's historical capacity data and environmental factors such as temperature and charge and discharge rate information, the data is first normalized to eliminate the impact of dimensions and unify data standards. Then, a multivariate linear regression model is used for modeling and analysis. The model is built using R language or Python's Pandas and Sci-kitlearn libraries. Data scientists select appropriate variables for regression analysis based on different working conditions of the battery, such as analyzing how rising temperature affects the rapid decline in battery capacity, or how charge and discharge rates affect the long-term health of the battery. Through analysis, the rate of battery capacity decay under different conditions can be estimated, thereby drawing a power capacity decay curve that reflects the overall health status of the battery. This curve helps the technical team understand the battery's decay trend, optimize the battery's usage strategy and maintenance plan, and thus extend the battery's service life. The resulting power capacity decay curve is an important reference for predicting future battery performance.

[0104] See also Figure 4 ,The steps to obtain the prediction results of power capacity attenuation trend are as follows:

[0105] S221: Perform data analysis on the power capacity attenuation curve, identify the mutation points and trend turning points on the curve, reveal the moment of battery performance mutation, and generate key change point data;

[0106] First, the power capacity attenuation curve data is accurately analyzed, and sensitivity parameters are set, such as the minimum difference threshold and sliding window size. These parameters allow data changes to be detected at different scales, thereby identifying key mutation points and trend turning points on the curve. Mutation points usually manifest as significant changes in battery performance, such as a rapid decrease or recovery in battery capacity. Each detected point will be verified through time series analysis to exclude false positive results and ensure that only real performance change points are recorded. The generated key change point data contains all key time points and their corresponding battery capacity values, providing accurate data support for the next step of trend analysis and battery maintenance strategy formulation.

[0107] S222: Analyze the data trends of different time periods based on the key change point data, separate the long-term and short-term decay trends, and generate separated key decay trend data;

[0108] Apply trend separation such as piecewise linear regression to process key change point data. By defining the data range of each segment, the trend of the data in the segment is systematically analyzed, including the calculation of the slope and intercept. This method can effectively distinguish the long-term and short-term trends of battery performance attenuation. In addition, it also includes an optimization process. The parameters of each segment are adjusted by the least squares method to maximize the accuracy of data fitting. Not only the main trend lines are accurately separated, but also the statistical significance of each trend is evaluated to ensure that the resulting attenuation trend reflects the actual performance changes of the battery. Detailed and separated trend data is very critical and will directly affect the simulation and prediction of the future performance of the battery, providing a solid data foundation for battery management and optimization decisions.

[0109] S223: Based on the separated key attenuation trend data, predict the future capacity change, analyze and determine the future attenuation path, and obtain the power capacity attenuation trend prediction result;

[0110] Based on the separated key attenuation trend data, support vector machine (SVM) was selected as the prediction model. SVM was selected for its advantages in small sample, nonlinear and high-dimensional data processing. The model was trained using historical trend data to ensure that the model can learn the complex pattern of battery capacity attenuation. Afterwards, the newly separated trend data was input into the trained model for testing and verification. The model uses the data to predict the future capacity changes of the battery. The prediction results not only show the battery's attenuation rate, but also predict the possible degree of attenuation. The prediction results are crucial for formulating battery maintenance strategies, extending battery life and optimizing battery usage conditions, and provide quantitative data support and a scientific basis for decision-making.

[0111] See also Figure 5 ,The analysis steps of the correlation between environmental factors and battery performance degradation are,

[0112] S311: Based on the power capacity attenuation trend prediction result, the current ambient temperature and humidity are synchronously collected to obtain synchronous environmental data;

[0113] Data synchronization is performed through the existing power capacity attenuation trend prediction results, that is, the current temperature and humidity data are collected from the environmental monitoring equipment, and automated data collection is used to ensure accurate data collection and real-time transmission. The system automatically records data every ten minutes and marks the specific time of each data point to ensure the timing correctness of subsequent analysis. The collected data is initially cleaned, including filtering out abnormal values ​​or possible erroneous readings, such as data points that exceed the normal measurement range. This is achieved by setting thresholds. Any temperature or humidity readings that exceed the set threshold will be automatically eliminated. The cleaned data is stored in the central database and checked for data integrity and consistency. In addition, the system also associates environmental data with power capacity data to establish a data connection for subsequent correlation analysis.

[0114] S312: Use the synchronized environment data to perform correlation analysis, using the formula,

[0115]

[0116] Calculate the correlation coefficient r between the ambient temperature and humidity and the battery capacity decay rate to obtain a correlation coefficient matrix, where x and y represent the data values ​​of the collected environmental data and the battery performance decay rate, and n is the number of data points;

[0117] The following data sets are collected, taking temperature and battery performance degradation as examples:

[0118] x = [21, 22, 21, 23, 24] (degrees Celsius).

[0119] y = [5.0, 4.8, 5.1, 4.9, 4.7] (attenuation percentage).

[0120] Let n=5 (number of data points).

[0121] First calculate the elements needed for each item:

[0122] ∑x=21+22+21+23+24=111

[0123] ∑y=5.0+4.8+5.1+4.9+4.7=24.5

[0124] ∑xy=21×5.0+22×4.8+21×5.1+23×4.9+24×4.7

[0125] =105+105.6+107.1+112.7+112.8=543.2

[0126] ∑x 2 =21 2 +22 2 +21 2 +23 2 +24 2

[0127] =441+484+441+529+576=2471

[0128] ∑y 2 =5.0 2 +4.8 2 +5.1 2 +4.9 2 +4.7 2

[0129] =25+23.04+26.01+24.01+22.09=120.15

[0130] Then substitute the correlation coefficient formula to calculate:

[0131] molecular:

[0132] 5×543.2-111×24.5=2716-2719.5=-3.5

[0133] Denominator:

[0134]

[0135] Correlation coefficient r:

[0136]

[0137] The results show that there is a slight negative correlation (-0.849) between temperature and battery capacity decay, which means that as the temperature increases, the battery performance decay rate decreases slightly. The analysis results are important for understanding how environmental factors affect battery performance decay and can be used to guide future battery use and maintenance strategies.

[0138] S313: using the correlation coefficient matrix, identifying key environmental factors that affect battery performance degradation, and forming a correlation analysis record between environmental factors and battery performance degradation;

[0139] The correlation coefficient matrix is ​​used to identify key environmental factors that have a significant impact on battery performance degradation, mainly by comparing the correlation coefficients of various environmental factors with the preset significance threshold. This analysis process involves complex data processing and statistical judgment. The correlation coefficient of each environmental factor is compared with the threshold one by one, and factors exceeding the threshold are marked as key influencing factors. The identification of key factors is based on continuous observation and long-term data accumulation, which ensures the reliability and scientificity of the analysis results. The analysis results will be recorded in detail in the correlation analysis record between environmental factors and battery performance degradation. Not only each key factor is listed, but also the specific degree of impact and possible mechanisms are described in detail, which provides a scientific basis for the use and maintenance of batteries and will be used to guide the battery maintenance team to adjust the battery use environment or improve the battery design to optimize the overall performance of the battery and extend its service life.

[0140] See also Figure 6 , the steps to obtain environmental impact analysis records are:

[0141] S321: Organize differentiated environmental experiments based on the correlation analysis record between environmental factors and battery performance degradation, record experimental data in real time and automatically store them, and obtain differentiated experimental records;

[0142] Based on the previously obtained analysis report on the correlation between environmental factors and battery performance attenuation, the temperature and humidity ranges with the most significant impact were selected as experimental variables, and different environmental simulation conditions were established in the laboratory to carry out detailed battery performance tests. Specifically, the temperature control equipment in the laboratory was set to vary between 22°C and 35°C, and the humidity control was adjusted between 45% and 75% to ensure the diversity of the simulated environment and the extensiveness of the test. High-precision sensors and automatic recording equipment were used to continuously monitor the charging efficiency, discharge rate and total capacity of the battery under each setting. At least five rounds of tests were carried out under each environmental condition to ensure the stability and repeatability of the data. Real-time data was automatically uploaded and recorded through the data acquisition system, and then used for detailed performance analysis, which not only helped to confirm the operating efficiency of the battery under different environmental conditions, but also to evaluate its reliability under extreme conditions.

[0143] S322: performing statistical analysis on the collected power supply performance data according to the difference experiment records, evaluating the impact of the change of the difference environmental conditions on the power supply performance, and obtaining the environmental condition statistical analysis results;

[0144] ANOVA was used to determine whether the effects of temperature and humidity changes on battery performance were statistically significant. The formula is: Among them, MS between is the mean square error between groups, representing the performance differences between different environmental conditions, MS withinis the within-group mean square error, representing the natural variation in performance under the same environmental conditions. Determine the data:

[0145] MS between =22.5(performance difference)

[0146] MS within =15.3 (natural variation)

[0147] Calculate the F value:

[0148]

[0149] This F value will be compared with the critical F value to determine whether the impact of environmental variables is significant; if the F value is greater than the critical value, it indicates that environmental factors have a significant impact on battery performance. This result shows that changes in temperature and humidity do affect the charging and discharging efficiency of the battery.

[0150] S323: Based on the statistical analysis results of environmental conditions, the changes in power supply performance under each environmental condition and the actual impact of environmental factors are sorted out, and an environmental impact analysis record is output;

[0151] After collecting and analyzing the power supply performance data under various environmental conditions, the information is integrated into an environmental impact analysis record, which details how changes in temperature and humidity affect power supply performance; the power supply performance data under each test environment is integrated and compared to generate detailed data tables and graphical analysis results, including statistical data of variance analysis, significance levels and changing trends in power supply performance, which not only provides the R&D team with performance evaluation of batteries under different environmental conditions, but also supports more accurate battery design and improvement decisions; in addition, the environmental impact analysis record includes recommendations for power supply maintenance and optimization measures, guiding the technical team to consider environmental factors in power supply design, optimize the environmental adaptability and long-term stability of the product, and thereby improve the competitiveness of the power supply in the market and user satisfaction.

[0152] See also Figure 7 , the steps to obtain the optimization solution of portable energy storage power supply are:

[0153] S411: Use the environmental impact analysis records to evaluate the impact of different environmental factors on the charging and discharging efficiency of energy storage power supplies, extract the charging and discharging data under each environment, and use the formula,

[0154]

[0155] Calculate the average efficiency η under each condition and obtain the efficiency analysis results, where c i is the charging efficiency under the i-th environment, d i is the discharge efficiency under the ith environment, v iis the voltage under the target environmental condition, M is the number of differential environmental conditions;

[0156] The data collected in three environments are:

[0157] c1=0.8,c2=0.85,c3=0.78,

[0158] d1=0.75,d2=0.8,d3=0.7,

[0159] v1=1.2,v2=1.0,v3=1.1

[0160] M=3

[0161] Substituting the values ​​into the formula, we get:

[0162]

[0163] The results show that under given environmental conditions, the average efficiency of the energy storage system is 66.69%, which is obtained through actual data monitoring and calculation, and is closely related to the step results, guiding us to make the next strategy adjustment.

[0164] S412: according to the efficiency analysis result, a comparison threshold is set to determine the key environmental conditions with low efficiency, and the charging and discharging parameters of the power supply are adjusted according to the corresponding environmental conditions to match the difference environmental conditions and obtain an adjustment strategy;

[0165] After analyzing the environmental impact records, comparison thresholds were set to identify key environmental factors with low efficiency, mainly including high temperature and high humidity, which have a negative impact on the battery's charge and discharge performance. Based on relevant data, specific operational measures for adjusting the charge and discharge parameters were further formulated. The measures include adjusting the charging rate and modifying the discharge depth to adapt to different environmental conditions. For example, the charging rate is reduced in a high temperature environment to reduce heat loss, and the discharge depth is increased in a high humidity environment to make full use of the battery capacity. The final adjustment strategy can ensure that the power system maintains an optimized operating state under adverse conditions. The implementation of improvement measures is a strategy formed after comprehensive analysis of the data obtained from field environmental tests and simulations.

[0166] S413: Redesign the structure and control strategy of the portable energy storage power supply according to the adjustment strategy, and evaluate the actual impact on the performance to form an optimization plan for the portable energy storage power supply;

[0167] Based on the above analysis and strategy adjustment results, the design of the portable energy storage power supply was optimized, including structural adjustment and update of control strategy, including adding a battery insulation layer to reduce the impact of ambient temperature on battery performance, dynamically adjusting the charging and discharging parameters, optimizing the operating efficiency according to actual environmental changes, and adjusting the performance of the optimized power supply through practical application verification and evaluation. The final optimization scheme effectively improved the overall performance and reliability. The optimization scheme was formulated based on the comprehensive environmental impact analysis and experimental data, ensuring the practicality and foresight of the design.

[0168] A portable energy storage power supply evaluation system, comprising:

[0169] The real-time monitoring module deploys sensors to measure the voltage, current, temperature and charge and discharge frequency of the power supply in real time, mark each data with a timestamp, calculate the fluctuation rate of the power supply performance, and obtain real-time performance monitoring data;

[0170] The state analysis module extracts the real-time voltage and current data of the battery under charge and discharge conditions based on real-time performance monitoring data, calculates the rate of change of voltage and current in each charge and discharge cycle, and draws and outputs the power capacity attenuation curve based on the historical capacity data of the battery and environmental factors;

[0171] The trend prediction module performs data analysis on the power capacity attenuation curve, identifies the mutation points and trend turning points on the curve, analyzes the data trends of different time periods, analyzes and determines the future attenuation path, and obtains the power capacity attenuation trend prediction results;

[0172] The environment correlation module uses the power capacity attenuation trend prediction results to simultaneously collect the current ambient temperature and humidity, perform correlation analysis, identify key environmental factors that affect battery performance attenuation, and output the correlation analysis records between environmental factors and battery performance attenuation;

[0173] The experimental evaluation module organizes differentiated environmental experiments based on the correlation analysis records between environmental factors and battery performance degradation, conducts statistical analysis, evaluates the impact of changes in different environmental conditions on power performance, and organizes and outputs environmental impact analysis records;

[0174] The optimization scheme module uses the environmental impact analysis records to evaluate the impact of different environmental factors on the charging and discharging efficiency of the energy storage power supply, calculates the average efficiency under each condition, adjusts the charging and discharging parameters of the power supply, matches the different environmental conditions, redesigns the portable energy storage power supply structure and control strategy, and forms an optimization scheme for the portable energy storage power supply.

[0175] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A portable energy storage power supply evaluation method, characterized in that: The following steps are involved: The sensors collect the power supply voltage, current, temperature and charge and discharge frequency data, record the data stream in real time in the local database, analyze the current working status of the power supply, and generate real-time performance monitoring data; By using the real-time performance monitoring data, the changes in voltage and current of the battery during each charge and discharge are calculated, a power capacity attenuation curve is drawn, key attenuation trends are separated according to the power capacity attenuation curve, and future capacity changes of the power supply are predicted to generate a power capacity attenuation trend prediction result; Using the power capacity attenuation trend prediction results, synchronously collect environmental temperature and humidity data, analyze the correlation between environmental factors and battery performance attenuation, test the differences in energy storage power supply performance under different environmental conditions based on the analysis results, confirm the actual impact of the difference factors on power supply performance, and form an environmental impact analysis record; Through the environmental impact analysis records, the charging and discharging strategies of the energy storage power supply are dynamically adjusted, and improvement measures are designed for key problem points to form an optimization plan for the portable energy storage power supply.

2. The portable energy storage power supply evaluation method according to claim 1, characterized in that: The step of acquiring the real-time performance monitoring data is: Deploy sensors to measure the voltage, current, temperature, and charge and discharge frequency of the power supply in real time to obtain a real-time power supply data set; Uploading the real-time power data set to a local database, marking each piece of data with a timestamp, and obtaining a power data record with a timestamp; Through the power supply data recording with time stamp, the data is cleaned and analyzed, using the formula, Calculate the fluctuation rate P of power supply performance and obtain real-time performance monitoring data, where a i represents a single data point, μ represents the data mean, and N represents the total number of data points.

3. The portable energy storage power supply evaluation method according to claim 2, characterized in that: The steps of drawing the power capacity attenuation curve are as follows: Based on the real-time performance monitoring data, extract the real-time voltage and current data of the battery under the charging and discharging state to obtain a real-time data set of voltage and current; Using the voltage and current real-time data set, the formula is used, and Calculate the rate of change of voltage and current in each charge and discharge cycle to obtain the change sequence data of voltage and current, where V i and I i Represent the voltage and current values ​​measured for the ith time, V i+1 is the voltage measurement at time i+1, I i+1 is the current measurement at time i+1, ΔV i Represents the voltage change rate from time i to time i+1, ΔI i Represents the square root ratio of the current change from time i to time i+1; The voltage and current variation sequence data are used to perform a multivariate linear regression analysis, and the power capacity attenuation curve is drawn and output in combination with the historical capacity data of the battery and environmental factors.

4. The portable energy storage power supply evaluation method according to claim 3, characterized in that: The steps for obtaining the power capacity attenuation trend prediction result are as follows: Performing data analysis on the power capacity attenuation curve, identifying mutation points and trend turning points on the curve, revealing the moment of battery performance mutation, and generating key change point data; Based on the key change point data, analyzing the data trends of different time periods, separating long-term and short-term decay trends, and generating separated key decay trend data; Based on the separated key attenuation trend data, a prediction of future capacity changes is performed, and the future attenuation path is analyzed and determined to obtain a power supply capacity attenuation trend prediction result.

5. The portable energy storage power supply evaluation method according to claim 4, characterized in that: The analysis steps of the correlation between the environmental factors and the battery performance degradation are as follows: Based on the power capacity attenuation trend prediction result, the current ambient temperature and humidity are synchronously collected to obtain synchronous environmental data; Using the synchronized environment data, correlation analysis is performed using the formula: Calculate the correlation coefficient r between the ambient temperature and humidity and the battery capacity decay rate to obtain a correlation coefficient matrix, where x and y represent the data values ​​of the collected environmental data and the battery performance decay rate, and n is the number of data points; The correlation coefficient matrix is ​​used to identify key environmental factors that affect battery performance degradation, and to form a correlation analysis record between environmental factors and battery performance degradation.

6. The portable energy storage power supply evaluation method according to claim 5, characterized in that: The steps for obtaining the environmental impact analysis record are: According to the correlation analysis record between the environmental factors and battery performance degradation, organize differentiated environmental experiments, record experimental data in real time and automatically store them, and obtain differential experimental records; According to the difference experiment records, statistical analysis is performed on the collected power supply performance data to evaluate the impact of changes in the difference environmental conditions on the power supply performance, and obtain environmental condition statistical analysis results; Based on the statistical analysis results of the environmental conditions, the changes in power supply performance under each environmental condition and the actual impact of environmental factors are sorted out, and an environmental impact analysis record is output.

7. The portable energy storage power supply evaluation method according to claim 6, characterized in that: The steps for obtaining the portable energy storage power supply optimization solution are: Using the environmental impact analysis records, evaluate the impact of different environmental factors on the charging and discharging efficiency of energy storage power supplies, extract the charging and discharging data under each environment, and use the formula, Calculate the average efficiency η under each condition and obtain the efficiency analysis results, where c i is the charging efficiency under the i-th environment, d i is the discharge efficiency under the ith environment, v i is the voltage under the target environmental condition, M is the number of differential environmental conditions; According to the efficiency analysis result, a comparison threshold is set to determine the key environmental conditions with low efficiency, and the charging and discharging parameters of the power supply are adjusted according to the corresponding environmental conditions to match the difference environmental conditions and obtain an adjustment strategy; According to the adjustment strategy, the structure and control strategy of the portable energy storage power supply are redesigned, and the actual impact on the performance is evaluated to form an optimization plan for the portable energy storage power supply.

8. A portable energy storage power supply evaluation system, characterized in that: The system is used to execute the portable energy storage power supply evaluation method according to any one of claims 1 to 7, comprising: The real-time monitoring module deploys sensors to measure the voltage, current, temperature and charge and discharge frequency of the power supply in real time, mark each data with a timestamp, calculate the fluctuation rate of the power supply performance, and obtain real-time performance monitoring data; The state analysis module extracts the real-time voltage and current data of the battery under the charge and discharge state based on the real-time performance monitoring data, calculates the rate of change of the voltage and current in each charge and discharge cycle, and draws and outputs the power capacity attenuation curve in combination with the historical capacity data of the battery and environmental factors; The trend prediction module performs data analysis on the power capacity attenuation curve, identifies mutation points and trend turning points on the curve, analyzes data trends in different time periods, analyzes and determines future attenuation paths, and obtains power capacity attenuation trend prediction results; The environment correlation module collects the current environment temperature and humidity synchronously through the power capacity attenuation trend prediction result, performs correlation analysis, identifies key environment factors that affect battery performance attenuation, and outputs the correlation analysis record between environment factors and battery performance attenuation; The experimental evaluation module organizes differentiated environmental experiments based on the correlation analysis records between the environmental factors and battery performance degradation, performs statistical analysis, evaluates the impact of changes in differential environmental conditions on power performance, and organizes and outputs environmental impact analysis records; The optimization scheme module uses the environmental impact analysis records to evaluate the impact of different environmental factors on the charging and discharging efficiency of the energy storage power supply, calculates the average efficiency under each condition, adjusts the charging and discharging parameters of the power supply, matches the different environmental conditions, redesigns the portable energy storage power supply structure and control strategy, and forms a portable energy storage power supply optimization scheme.

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